source the R script, make.R to generate the project document.
> source("make.R")This document was generated on 2016-01-13 09:34:59.
1 Project repository
Live version is stored here. Static version is this document.
- Main
- Cognitive Impairment adverse event
This document is for the Cognitive Impairment topic.
For other topics, see links from the project repository.
2 Scripts
Sequence of scripts:
prologue.Rmd %>% tidyData.Rmd %>% runMetaAnalysisPrePost.Rmd %>% epilogue.Rmd
3 Prologue
- Set path of input data sources
- Load
devtools - Source the
loadPkgfunction - Load packages
- Source the
makeMetadatafunction - Start the job timer
## Sourcing https://gist.githubusercontent.com/benjamin-chan/3b59313e8347fffea425/raw/84a146f3cde6330b901521710d513fa9d0b96951/loadPkg.R
## SHA-1 hash of file is 7bdcd4569a86aa9fff8ced241327992c550a16ce
## Sourcing https://gist.githubusercontent.com/benjamin-chan/091209ab4eee1f171540/raw/5043f40fb0c15036b0ce53079045d7d1beae5609/makeMetadata.R
## SHA-1 hash of file is 66a9fa7f31fa5e4e4448ed18f18db768a1c5a70f4 Tidy data
Sequence of scripts:
replicateOno.Rmd %>% readAhles.Rmd %>% readTager.Rmd %>% reshapeOno.Rmd %>% addFromDataAbstraction.Rmd %>% combineData.Rmd
4.1 Replicate Ono
Replicate data from Ono, Miyuki, et al. “A Meta-Analysis of Cognitive Impairment and Decline Associated with Adjuvant Chemotherapy in Women with Breast Cancer.” Front Oncol. 2015; 5: 59.
Data file was requested and received from the co-author, James Ogilvie, in October 2015.
The DOMAINFORMETAkvd field (column AG) was coded by Kathleen Van Dyk KVanDyk@mednet.ucla.edu.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Tuesday, November 03, 2015 5:08 PM
To: Ayse Tezcan
Cc: Benjamin Chan
Subject: RE: Cognitive impairment draft paper
Hi,
Attached is the Ono spreadsheet with a new column with my suggestions for
domains and domains for each Ahles test is in sheet 2. I've highlighted tests
that we may want to exclude if we want to consistently keep one or two
measures per test. Ben --- does it matter statistically if there is more than
one measure from the same test (for example delayed recall and delayed
recognition) in the same domain? In almost every case we have total and delay
for memory tests but if we add in more measures (Trial 6, Supraspan,
Recognition) does this confound analyses because these are likely highly
correlated measures within the same test? Would all of the studies need to
use the same measures in each test (i.e., every study uses Total and Delay)?
I might not be asking this clearly --- let me know what you think.Read data file.
f <- sprintf("%s/%s", pathIn, "Requested Chemo Data domains kvd 11.19.15 2.xlsx")
echoFile(f)## File: StudyDocuments/Requested Chemo Data domains kvd 11.19.15 2.xlsx
## Modification date: 2015-12-10 07:44:33
## File size: 178.9 KBD0 <- read.xlsx(f, sheet=1, check.names=TRUE)
D0 <- data.table(D0)Show a map of the column names and locations.
colNames <- data.frame(colNum = 1:ncol(D0),
colCell = c(LETTERS,
sprintf("%s%s", LETTERS[1], LETTERS),
sprintf("%s%s", LETTERS[2], LETTERS),
sprintf("%s%s", LETTERS[3], LETTERS))[1:ncol(D0)],
varName = names(D0))
colNames## colNum colCell varName
## 1 1 A First.Auth
## 2 2 B Study.Ref
## 3 3 C Pub.Year
## 4 4 D Cog.Test
## 5 5 E DOMAIN.FOR.META..kvd.
## 6 6 F Journal
## 7 7 G Pre.Meta
## 8 8 H Design
## 9 9 I Comp.Grp
## 10 10 J Healthy_GROUP
## 11 11 K Tx.Grp
## 12 12 L Pre.Post.Time.Interval
## 13 13 M Time.SD
## 14 14 N Tx.N
## 15 15 O Ctl.N
## 16 16 P Total.N
## 17 17 Q Tx.Age
## 18 18 R Tx.Age.SD
## 19 19 S Ctl.Age
## 20 20 T Ctl.Age.SD
## 21 21 U Tx.IQ
## 22 22 V Tx.IQ.SD
## 23 23 W Ctl.IQ
## 24 24 X Ctl.IQ.SD
## 25 25 Y IQ.Note
## 26 26 Z Tx.EDU
## 27 27 AA Tx.EDU.SD
## 28 28 AB Ctl.EDU
## 29 29 AC Ctl.EDU.SD
## 30 30 AD EDU.Note
## 31 31 AE Tx.Chem.Time
## 32 32 AF Tx.Chem.Time.SD
## 33 33 AG Cognitive.Domain.Primary
## 34 34 AH Score.Typ
## 35 35 AI Tx.M
## 36 36 AJ Tx.SD
## 37 37 AK Ctl.M
## 38 38 AL Ctl.SD
## 39 39 AM Direct.Notes
## 40 40 AN X1.X2
## 41 41 AO Tx.N.1
## 42 42 AP Ctl.N.1
## 43 43 AQ Tx.SD.2
## 44 44 AR Ctl.SD.2
## 45 45 AS Spooled
## 46 46 AT Cohen.s.d
## 47 47 AU Hedges.g
## 48 48 AV Var1
## 49 49 AW Var2
## 50 50 AX Variance
## 51 51 AY Standard.Error
## 52 52 AZ Weight
## 53 53 BA w.ES
## 54 54 BB w.ES.2
## 55 55 BC w.2
## 56 56 BD StudyES
## 57 57 BE StudySE
## 58 58 BF z
## 59 59 BG LowerCI
## 60 60 BH UpperCI
## 61 61 BI Q
## 62 62 BJ df
## 63 63 BK Q.Critical
## 64 64 BL Q.Sig...p..05.
## 65 65 BM RANDOM.EFFECT
## 66 66 BN RE_w
## 67 67 BO w.ES.1
## 68 68 BP w.ES.2.1
## 69 69 BQ w.2.1
## 70 70 BR StudyES.1
## 71 71 BS StudySE.1
## 72 72 BT z.1
## 73 73 BU LowerCI.1
## 74 74 BV UpperCI.1
## 75 75 BW Q.1
## 76 76 BX df.1
## 77 77 BY Q.Critical.1
## 78 78 BZ Q.Sig...p..05..1
## 79 79 CA I.2.Fixed
## 80 80 CB I.2.RandomPut the summary rows in a separate data table, DOno.
DOno <- D0[is.na(First.Auth) & !is.na(Weight), c(52:ncol(D0)), with=FALSE]Put the instrument-level rows in a separate data table, D. Only keep the columns needed to calculate fixed and random effects statistics.
The RANDOM.EFFECT column was specific to the Ono analysis. The value in the Ono spreadsheet will be different for our use.
From: James Ogilvie [j.ogilvie@griffith.edu.au]
Sent: Sunday, October 18, 2015 5:42 PM
To: Benjamin Chan
Cc: 'jamelnikow@ucdavis.edu'; 'm.ono@griffith.edu.au';
'd.shum@griffith.edu.au'; Ayse Tezcan (aztezcan@ucdavis.edu); Meghan Soulsby
(masoulsby@ucdavis.edu)
Subject: Re: Fwd: request for data from your recently published meta-analysis
Hi Benjamin,
Thanks for contacting me regarding this issue. I had wondered whether Dr.
Melnikow had received the data I had sent, as I had not received confirmation
of my email containing the data.
These are very good questions! It took me a while to get my head around the
random effect model when performing this analysis. I am attaching an article
that I found very useful in coming to terms with the model - hopefully you
will find this useful too.
To answer your questions, is a constant across a pool of studies that you
wish to examine and generate summary/aggregate statistics (e.g., grand mean
effect size). Therefore, the value of the constant will change depending on
the the specific pool of studies examined. It is calculated across the total
pool of studies.
is the total Q statistic (assessing heterogeneity) that is calculated across
ALL studies and relates to the grand mean effect size. It is not the same as
the Q statistic in column BH. There is a Q statistic for each study (this is
the Q in column BH), as well as a Q statistic for all studies pooled
together (this being thestatistic). The formula for calculating the Q
statistic are provided in the pdf I've attached titled "Heterogeneity in
MA".
As I've mentioned, the value of is specific to the pool of studies you are
examining. Therefore, the value to calculate effect sizes according to a
random effects model will be different for your analyses - assuming you have a
different pool of studies that you are including in the analyses. Given this,
the value in column BL needs to be updated by you to be specific to the pool
of studies you are looking at. importantVar <- c(1, 9:12, 14:17, 19, 35:39, 65, 33, 4, 5, 34)
authors <- c("Bender", "Collins", "Jenkins", "Wefel")
D <- D0[First.Auth %in% authors, importantVar, with=FALSE]
setnames(D,
names(D),
c("author",
"comparisonGroup",
"healthyGroup",
"treatmentGroup",
"timeDays",
"nGroup1",
"nGroup2",
"nTotal",
"ageGroup1",
"ageGroup2",
"meanGroup1",
"sdGroup1",
"meanGroup2",
"sdGroup2",
"direction",
"randomEffect", # Keep the value from Ono for verification purposes; do not use for analysis
gsub("\\.", "", names(D0)[c(33, 4, 5, 34)])))
setnames(D, "DOMAINFORMETAkvd", "CognitiveDomainForMetaAnalysis")The data in the received file is in the form of longitudinal means and standard deviations. Do not show
DReplicate spreadsheet calculations.
D <- D[direction == "Lower worse",
`:=` (diffMean = meanGroup2 - meanGroup1)]
D <- D[direction == "Greater worse",
`:=` (diffMean = meanGroup1 - meanGroup2)]
D <- D[,
`:=` (sdPooled = sqrt((((nGroup1 - 1) * (sdGroup1 ^ 2)) +
((nGroup2 - 1) * (sdGroup2 ^ 2))) /
(nGroup1 + nGroup2 - 2)))]
D <- D[,
`:=` (cohenD = diffMean / sdPooled)]
D <- D[,
`:=` (hedgesG = cohenD * (1 - (3 / ((4 * nTotal) - 9))))]
D <- D[,
`:=` (var1 = (nGroup1 + nGroup2) / (nGroup1 * nGroup2),
var2 = hedgesG ^ 2 / (2 * (nGroup1 + nGroup2)))]
D <- D[,
`:=` (variance = var1 + var2)]
D <- D[,
`:=` (se = sqrt(variance),
weightFE = 1 / variance)]
D <- D[,
`:=` (effSizeWeightedFE = weightFE * hedgesG)]
D <- D[, weightRE := 1 / (variance + randomEffect)]
D <- D[, effSizeWeightedRE := weightRE * hedgesG]Calculate fixed effects statisitcs.
DFixed <- D[!is.na(nTotal),
.(df = .N,
sumWeights = sum(weightFE),
effSize = sum(effSizeWeightedFE) / sum(weightFE),
se = sqrt(1 / sum(weightFE)),
sumEffSizeWeighted = sum(effSizeWeightedFE),
ssEffSizeWeighted = sum(weightFE * hedgesG ^ 2),
ssWeights = sum(weightFE ^ 2)),
.(author, timeDays)]
DFixed <- DFixed[,
`:=` (z = effSize / se,
lowerCI = effSize + qnorm(0.025) * se,
upperCI = effSize + qnorm(0.975) * se,
Q = ssEffSizeWeighted - (sumEffSizeWeighted ^ 2 / sumWeights),
criticalValue = qchisq(0.05, df, lower.tail=FALSE))]
DFixed <- DFixed[,
`:=` (pvalue = pchisq(Q, df, lower.tail=FALSE),
Isq = 100 * ((Q - df) / Q))]Check if my calculations agree with Ono’s.
isCheckFixedPassed <- all.equal(DOno[, .(StudyES, z, Q)],
DFixed[, .(effSize, z, Q)],
check.names=FALSE)
message(sprintf("Do my FIXED effect statistic calculations agree with Ono's? %s",
isCheckFixedPassed))## Do my FIXED effect statistic calculations agree with Ono's? TRUEprint(xtable(DFixed), type="html")| author | timeDays | df | sumWeights | effSize | se | sumEffSizeWeighted | ssEffSizeWeighted | ssWeights | z | lowerCI | upperCI | Q | criticalValue | pvalue | Isq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Bender | 182.62 | 16 | 96.80 | 1.02 | 0.10 | 98.53 | 241.49 | 626.88 | 10.01 | 0.82 | 1.22 | 141.21 | 26.30 | 0.00 | 88.67 |
| 2 | Bender | 547.50 | 16 | 63.53 | 0.55 | 0.13 | 34.76 | 285.90 | 290.59 | 4.36 | 0.30 | 0.79 | 266.88 | 26.30 | 0.00 | 94.00 |
| 3 | Collins | 537.90 | 23 | 604.56 | 0.21 | 0.04 | 124.90 | 39.49 | 15893.07 | 5.08 | 0.13 | 0.29 | 13.69 | 35.17 | 0.94 | -68.01 |
| 4 | Collins | 146.50 | 23 | 607.30 | 0.10 | 0.04 | 58.14 | 17.63 | 16035.80 | 2.36 | 0.02 | 0.18 | 12.07 | 35.17 | 0.97 | -90.58 |
| 5 | Jenkins | 364.00 | 14 | 592.05 | 0.08 | 0.04 | 47.58 | 23.62 | 25038.13 | 1.96 | -0.00 | 0.16 | 19.79 | 23.68 | 0.14 | 29.27 |
| 6 | Jenkins | 28.00 | 14 | 593.46 | 0.03 | 0.04 | 19.46 | 12.29 | 25157.32 | 0.80 | -0.05 | 0.11 | 11.65 | 23.68 | 0.63 | -20.17 |
| 7 | Wefel | 182.62 | 10 | 89.09 | 0.18 | 0.11 | 15.73 | 5.24 | 793.76 | 1.67 | -0.03 | 0.38 | 2.47 | 18.31 | 0.99 | -305.57 |
| 8 | Wefel | 547.86 | 10 | 79.52 | 0.26 | 0.11 | 20.75 | 8.62 | 632.66 | 2.33 | 0.04 | 0.48 | 3.20 | 18.31 | 0.98 | -212.29 |
Calculate random effects statisitcs.
DRandom <- D[!is.na(nTotal),
.(df = .N,
sumWeights = sum(weightRE),
ssEffSizeWeighted = sum(weightRE * hedgesG ^ 2),
ssWeights = sum(weightRE ^ 2),
sumEffSizeWeighted = sum(effSizeWeightedRE),
effSize = sum(effSizeWeightedRE) / sum(weightRE),
se = sqrt(1 / sum(weightRE))),
.(author, timeDays)]
DRandom <- DRandom[,
`:=` (z = effSize / se,
lowerCI = effSize + qnorm(0.025) * se,
upperCI = effSize + qnorm(0.975) * se,
Q = ssEffSizeWeighted - (sumEffSizeWeighted ^ 2 / sumWeights),
criticalValue = qchisq(0.05, df, lower.tail=FALSE))]
DRandom <- DRandom[,
`:=` (pvalue = pchisq(Q, df, lower.tail=FALSE),
Isq = 100 * ((Q - df) / Q))]Check if my calculations agree with Ono’s.
isCheckRandomPassed <- all.equal(DOno[, c(19, 21, 24), with=FALSE],
DRandom[, .(effSize, z, Q)],
check.names=FALSE)
message(sprintf("Do my RANDOM effect statistic calculations agree with Ono's? %s",
isCheckRandomPassed))## Do my RANDOM effect statistic calculations agree with Ono's? TRUEprint(xtable(DRandom), type="html")| author | timeDays | df | sumWeights | ssEffSizeWeighted | ssWeights | sumEffSizeWeighted | effSize | se | z | lowerCI | upperCI | Q | criticalValue | pvalue | Isq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Bender | 182.62 | 16 | 48.04 | 137.29 | 147.43 | 53.60 | 1.12 | 0.14 | 7.73 | 0.83 | 1.40 | 77.50 | 26.30 | 0.00 | 79.35 |
| 2 | Bender | 547.50 | 16 | 37.25 | 199.95 | 92.56 | 26.23 | 0.70 | 0.16 | 4.30 | 0.38 | 1.03 | 181.48 | 26.30 | 0.00 | 91.18 |
| 3 | Collins | 537.90 | 23 | 115.28 | 7.62 | 577.83 | 23.96 | 0.21 | 0.09 | 2.23 | 0.03 | 0.39 | 2.64 | 35.17 | 1.00 | -770.38 |
| 4 | Collins | 146.50 | 23 | 115.38 | 3.38 | 578.84 | 11.11 | 0.10 | 0.09 | 1.03 | -0.09 | 0.28 | 2.31 | 35.17 | 1.00 | -895.09 |
| 5 | Jenkins | 364.00 | 14 | 75.63 | 3.04 | 408.57 | 6.11 | 0.08 | 0.11 | 0.70 | -0.14 | 0.31 | 2.54 | 23.68 | 1.00 | -450.68 |
| 6 | Jenkins | 28.00 | 14 | 75.65 | 1.57 | 408.83 | 2.50 | 0.03 | 0.11 | 0.29 | -0.19 | 0.26 | 1.49 | 23.68 | 1.00 | -840.71 |
| 7 | Wefel | 182.62 | 10 | 36.53 | 2.16 | 133.47 | 6.48 | 0.18 | 0.17 | 1.07 | -0.15 | 0.50 | 1.01 | 18.31 | 1.00 | -890.02 |
| 8 | Wefel | 547.86 | 10 | 34.81 | 3.79 | 121.21 | 9.12 | 0.26 | 0.17 | 1.55 | -0.07 | 0.59 | 1.41 | 18.31 | 1.00 | -611.34 |
Exclude tests Kathleen determined to be not useful.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Thursday, November 19, 2015 10:22 AM
To: Benjamin Chan
Cc: Ayse Tezcan
Subject: RE: Cognitive impairment draft paper
Hi Ben,
Ok --- attached is the Ono spreadsheet with my suggested domains. I did
strikethrough for the measures we probably shouldn't include at all in the
domains to keep it somewhat uniform across tests (i.e., some folks used Trial
1 from a list-learning test, some just used Total and Delay, etc.).strikethrough <- c("RAVL trial 6",
"CVLT Trial 1",
"RVLT trial 1",
"AVLT supraspan")
D <- D[!(CogTest %in% strikethrough)]Domains and tests.
unique(D[, .(CognitiveDomainForMetaAnalysis, CogTest)])[order(CognitiveDomainForMetaAnalysis, CogTest)]## CognitiveDomainForMetaAnalysis CogTest
## 1: Attn/Wkg Mem/Concentration WAIS-III -Arithmetic
## 2: Attn/Wkg Mem/Concentration 4WSTM 15 sec
## 3: Attn/Wkg Mem/Concentration 4WSTM 30 sec
## 4: Attn/Wkg Mem/Concentration 4WSTM 5 sec
## 5: Attn/Wkg Mem/Concentration Consonant trigrams
## 6: Attn/Wkg Mem/Concentration Letter-number sequencing: WAIS-III
## 7: Attn/Wkg Mem/Concentration PASAT number correct
## 8: Attn/Wkg Mem/Concentration Spatial span: WMS-III
## 9: Attn/Wkg Mem/Concentration TMT part A time
## 10: Attn/Wkg Mem/Concentration Trails A
## 11: Attn/Wkg Mem/Concentration WAIS-III Digit span
## 12: Attn/Wkg Mem/Concentration WAIS-III Letter-number sequencing
## 13: Attn/Wkg Mem/Concentration WAIS-R arithmetic
## 14: Attn/Wkg Mem/Concentration WAIS-R digit span
## 15: Attn/Wkg Mem/Concentration WMS-III digit span backwards
## 16: Attn/Wkg Mem/Concentration WMS-III digit span forward
## 17: Attn/Wkg Mem/Concentration WMS-III letter number sequencing
## 18: Attn/Wkg Mem/Concentration WMS-III spatial span backwards
## 19: Attn/Wkg Mem/Concentration WMS-III spatial span forwards
## 20: Exec Fxn Stroop
## 21: Exec Fxn TMT part B time
## 22: Exec Fxn Trails B
## 23: Exec Fxn WAIS-R similarities
## 24: Exec Fxn WCST sorts divided by trials
## 25: Information Proc Speed Letter cancellation
## 26: Information Proc Speed Symbol search: WAIS-III
## 27: Information Proc Speed WAIS-III Digit Symbol Coding
## 28: Information Proc Speed WAIS-III Symbol search
## 29: Information Proc Speed WAIS-R digit symbol
## 30: Motor Speed Grooved Peg Board time
## 31: Motor Speed Grooved pegboard dominant hand
## 32: Motor Speed Grooved pegboard nondominant hand
## 33: Verbal Ability/Language Boston Naming Test number correct
## 34: Verbal Ability/Language Verbal Fluency FAS number correct
## 35: Verbal Ability/Language Verbal fluency COWAT correct
## 36: Verbal Memory AVLT delayed
## 37: Verbal Memory AVLT total
## 38: Verbal Memory CVLT delayed recall
## 39: Verbal Memory CVLT delayed recognition
## 40: Verbal Memory RAVL delayed recall
## 41: Verbal Memory RAVL total score
## 42: Verbal Memory VSRT Delayed Recall
## 43: Verbal Memory VSRT Long-Term Storage
## 44: Verbal Memory WMS-III Logical memory II
## 45: Verbal Memory WMS-III Story delayed recall
## 46: Verbal Memory WMS-III Story immediate recall
## 47: Visual Memory Complex figure delayed
## 48: Visual Memory Complex figure immediate
## 49: Visual Memory NVSRT Delayed Recall
## 50: Visual Memory RCF delayed recall
## 51: Visual Memory RCF immediate recall
## 52: Visual Memory RVLT delayed recall
## 53: Visual Memory RVLT delayed recognition
## 54: Visual Memory WMS-III Family pictures II
## 55: Visuospatial WAIS-III Block design
## 56: Visuospatial WAIS-R block design
## CognitiveDomainForMetaAnalysis CogTestSave working data tables to file if the integrity checks passed. I don’t need to save DOno since the integrity checks passed.
metadataD = makeMetadata(D)
metadataDFixed = makeMetadata(DFixed)
metadataDRandom = makeMetadata(DRandom)
if (isCheckFixedPassed & isCheckRandomPassed) {
f <- sprintf("%s/%s", pathOut, "Ono.RData")
save(D,
metadataD,
DFixed,
metadataDFixed,
DRandom,
metadataDRandom,
file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))
} else {
warning(sprinf("Integrity checks failed.\n%s not saved.", f))
}## Output/Ono.RData saved on: 2016-01-13 09:35:02
## File size: 72.835 KB4.2 Read Ahles
Read data from Ahles TA, et al. “Longitudinal Assessment of Cognitive Changes Associated With Adjuvant Treatment for Breast Cancer: Impact of Age and Cognitive Reserve.” J Clin Oncol. 2010 Oct 10; 28(29): 4434-4440.
Data file was requested and received from the co-author, Yuelin Li, in October 2015.
Read data file (text format).
f <- sprintf("%s/%s", pathIn, "Soulsby_means.txt")
echoFile(f)## File: StudyDocuments/Soulsby_means.txt
## Modification date: 2015-12-10 07:44:33
## File size: 36.1 KBD <- fread(f, sep="|")The data in the received file is in the form of longitudinal means and standard deviations. Do not show
DStudy design.
D[, .(nrows = .N, totalN = sum(N)), .(txgrp, ptime)]## txgrp ptime nrows totalN
## 1: chemo baseline 35 2056
## 2: chemo posttx 35 1886
## 3: chemo 1yr 35 1677
## 4: chemo 2yr 35 1549
## 5: chemo no baseline 35 2432
## 6: chemo no posttx 35 2321
## 7: chemo no 1yr 35 2237
## 8: chemo no 2yr 35 2138
## 9: control baseline 35 1522
## 10: control posttx 35 1478
## 11: control 1yr 35 1447
## 12: control 2yr 35 1346Map ptime to months after treatment. Ahles TA, et al. reports results in terms of
- Baseline
- 1 month after treatment
- 6 months after treatment
- 18 months after treatment
As far as I can tell, values of ptime map to these, although seemingly imprecise.
D[ptime == "baseline", monthsPostTx := 0]
D[ptime == "posttx", monthsPostTx := 1]
D[ptime == "1yr", monthsPostTx := 6]
D[ptime == "2yr", monthsPostTx := 18]Exclude
- Non-chemotherapy and control patients
D <- D[txgrp == "chemo"]Instruments. Do not show
D[, .N, .(Variable, Label)]Merge Kathleen’s KVanDyk@mednet.ucla.edu domain assignments.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Tuesday, November 03, 2015 5:08 PM
To: Ayse Tezcan
Cc: Benjamin Chan
Subject: RE: Cognitive impairment draft paper
Hi,
Attached is the Ono spreadsheet with a new column with my suggestions for
domains and domains for each Ahles test is in sheet 2. I've highlighted tests
that we may want to exclude if we want to consistently keep one or two
measures per test. Ben --- does it matter statistically if there is more than
one measure from the same test (for example delayed recall and delayed
recognition) in the same domain? In almost every case we have total and delay
for memory tests but if we add in more measures (Trial 6, Supraspan,
Recognition) does this confound analyses because these are likely highly
correlated measures within the same test? Would all of the studies need to
use the same measures in each test (i.e., every study uses Total and Delay)?
I might not be asking this clearly --- let me know what you think.f <- sprintf("%s/%s", pathIn, "Requested Chemo Data domains kvd 11.19.15 2.xlsx")
echoFile(f)## File: StudyDocuments/Requested Chemo Data domains kvd 11.19.15 2.xlsx
## Modification date: 2015-12-10 07:44:33
## File size: 178.9 KBD0 <- read.xlsx(f, sheet=2, check.names=TRUE)
D0 <- data.table(D0)
CognitiveDomainForMetaAnalysis <- D0[!is.na(DOMAIN.FOR.META..kvd.), DOMAIN.FOR.META..kvd.]
lookup <- cbind(D[, .N, .(Variable, Label)], CognitiveDomainForMetaAnalysis)[, .(Variable, CognitiveDomainForMetaAnalysis)]
D <- merge(lookup, D, by="Variable")
unique(D[, .(CognitiveDomainForMetaAnalysis, Label)])[order(CognitiveDomainForMetaAnalysis, Label)]## CognitiveDomainForMetaAnalysis
## 1: Attn/Wkg Mem/Concentration
## 2: Attn/Wkg Mem/Concentration
## 3: Attn/Wkg Mem/Concentration
## 4: Attn/Wkg Mem/Concentration
## 5: Attn/Wkg Mem/Concentration
## 6: Attn/Wkg Mem/Concentration
## 7: Attn/Wkg Mem/Concentration
## 8: Attn/Wkg Mem/Concentration
## 9: Attn/Wkg Mem/Concentration
## 10: Attn/Wkg Mem/Concentration
## 11: Exec Fxn
## 12: Exec Fxn
## 13: Exec Fxn
## 14: Exec Fxn
## 15: Exec Fxn
## 16: Exec Fxn
## 17: Exec Fxn
## 18: Information Proc Speed
## 19: Information Proc Speed
## 20: Information Proc Speed
## 21: Information Proc Speed
## 22: Motor Speed
## 23: Motor Speed
## 24: Motor Speed
## 25: Verbal Ability/Language
## 26: Verbal Ability/Language
## 27: Verbal Ability/Language
## 28: Verbal Ability/Language
## 29: Verbal Memory
## 30: Verbal Memory
## 31: Verbal Memory
## 32: Verbal Memory
## 33: Visual Memory
## 34: Visual Memory
## 35: Visuospatial
## CognitiveDomainForMetaAnalysis
## Label
## 1: CPT: Distractibility, Correct Responses
## 2: CPT: Distractibility, False Positives
## 3: CPT: Distractibility, Reaction Time
## 4: CPT: Vigilance, Correct Responses
## 5: CPT: Vigilance, False Positives
## 6: CPT: Vigilance, Reaction Time
## 7: DKEFS Trails: Letter Sequencing, sec
## 8: DKEFS Trails: Number Sequencing, sec
## 9: PASAT (Rao): 2 second pacing
## 10: PASAT (Rao): 3 second pacing
## 11: DKEFS Card Sorting: Confirmed Correct Sorts
## 12: DKEFS Card Sorting: Free Sorting
## 13: DKEFS Stroop: Color-Word
## 14: DKEFS Trails: Number-Letter Switching, sec
## 15: DKEFS Verbal Fluency: Switching Fruits/Veget
## 16: DKEFS: Card Sorting, Sort Recognition
## 17: DKEFS: Stroop, Set Shifting
## 18: CVLT-2: Digit Symbol
## 19: DKEFS Stroop: Color Patch Naming
## 20: DKEFS Stroop: Word Reading, sec
## 21: DKEFS Trails: Visual Scanning in Seconds
## 22: DKEFS Trails: Motor Speed, sec
## 23: Grooved Pegboard Test: Left Hand, sec
## 24: Grooved Pegboard Test: Right Hand, sec
## 25: DKEFS Verbal Fluency
## 26: DKEFS Verbal Fluency: anival or clothing and names
## 27: WASI: Vocabulary
## 28: WRAT-3 Reading Score
## 29: CVLT-2: Long Delay Free Recall
## 30: CVLT-2: Trials 1-5 Total
## 31: Wechsler Memory Scale-3: Logical Memory I
## 32: Wechsler Memory Scale-3: Logical Memory II
## 33: Wechsler Memory Scale-3: Faces I
## 34: Wechsler Memory Scale-3: Faces II
## 35: WASI: Block Design
## LabelSave working data tables to file.
metadata <- makeMetadata(D)
f <- sprintf("%s/%s", pathOut, "Ahles.RData")
save(D, metadata, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Output/Ahles.RData saved on: 2016-01-13 09:35:02
## File size: 23.104 KB4.3 Read Tager
Read data from Tager, FA, et al. “The cognitive effects of chemotherapy in post-menopausal breast cancer patients: a controlled longitudinal study.” Breast Cancer Res Treat. 2010 Aug;123(1):25-34.
Data file was requested and received from the co-author, Paula S. McKinley, on November 20, 2015.
Read data file (SPSS format).
f <- sprintf("%s/%s", pathIn, "Tager_DataForMetaAnalysis.sav")
echoFile(f)## File: StudyDocuments/Tager_DataForMetaAnalysis.sav
## Modification date: 2015-12-10 07:44:33
## File size: 102.7 KBD <- read_sav(f)
D <- data.table(D)
D <- D[,
`:=` (session = factor(session,
levels = 1:4,
labels = c("Pre-surgery",
"Post surgery before treatment",
"Post treatment/6mths post surgery",
"6 month follow-up")),
chemoyn = factor(chemoyn,
levels= 0:1,
labels = c("No", "Yes")),
CTregmen = factor(CTregmen,
levels = 1:3,
labels = c("AC",
"ACT",
"CMF")),
tx = factor(tx,
levels = 0:11,
labels = c("None",
"Chemo",
"Radiation",
"Tamoxifen",
"Arimadex",
"Chemo + Radiation",
"Chemo + Tamoxifen",
"Chemo + Arimadex",
"Radiation + Tamoxifen",
"Radiation + Arimadex",
"Chemo + Radiation + Tamoxifen",
"Chemo + Radiation + Arimadex")))]Check data.
D[, .N, .(chemoyn, CTregmen)]## chemoyn CTregmen N
## 1: No NA 89
## 2: Yes CMF 24
## 3: Yes ACT 40
## 4: Yes AC 21D[, .N, .(chemoyn, chemowks)]## chemoyn chemowks N
## 1: No 88 89
## 2: Yes 24 39
## 3: Yes 16 14
## 4: Yes 12 8
## 5: Yes 28 3
## 6: Yes 18 3
## 7: Yes 8 12
## 8: Yes 14 6Keep z-score variables for these instruments.
- Finger Tapper - Dom Hand
- Finger Tapper - NonDom Hand
- Pegboard - Dom Hand
- Pegboard - Nondom Hand
- COWAT
- Boston Naming
- Trail Making A
- Trail Making B
- WAIS-III Digit Symbol
- WAIS-III Digit Span
- WAIS-III Arithmetic
- WAIS-III Number/Letter
- Rey Copy
- Buschke Total
- Benton Visual Retention Correct
measures <- c("tapdomz",
"tapndomz",
"pegdomz",
"pegndomz",
"cowz",
"bntz",
"trlaz",
"trlbz",
"dsymz",
"dspaz",
"aritz",
"numz",
"reyz",
"bustotz",
"bvrcoz")Melt data.
D <- melt(D,
id.vars = c("subid", "session", "chemoyn", "chemowks", "CTregmen", "tx"),
measure.vars = measures)
setnames(D, "variable", "Variable")
D <- D[Variable == "tapdomz", Label := "Finger Tapper - Dom Hand"]
D <- D[Variable == "tapndomz", Label := "Finger Tapper - NonDom Hand"]
D <- D[Variable == "pegdomz", Label := "Pegboard - Dom Hand"]
D <- D[Variable == "pegndomz", Label := "Pegboard - Nondom Hand"]
D <- D[Variable == "cowz", Label := "COWAT"]
D <- D[Variable == "bntz", Label := "Boston Naming"]
D <- D[Variable == "trlaz", Label := "Trail Making A"]
D <- D[Variable == "trlbz", Label := "Trail Making B"]
D <- D[Variable == "dsymz", Label := "WAIS-III Digit Symbol"]
D <- D[Variable == "dspaz", Label := "WAIS-III Digit Span"]
D <- D[Variable == "aritz", Label := "WAIS-III Arithmetic"]
D <- D[Variable == "numz", Label := "WAIS-III Number/Letter"]
D <- D[Variable == "reyz", Label := "Rey Copy"]
D <- D[Variable == "bustotz", Label := "Buschke Total "]
D <- D[Variable == "bvrcoz", Label := "Benton Visual Retention Correct"]
setkey(D, subid, session)Exclude
- Non-chemotherapy patients
- Measurements before surgery
D <- D[chemoyn != "No" &
session != "Pre-surgery"]
D[, .N, .(chemoyn, session)]## chemoyn session N
## 1: Yes Post surgery before treatment 450
## 2: Yes Post treatment/6mths post surgery 450
## 3: Yes 6 month follow-up 375Calculate means and standard deviations
T <- D[,
.(N = .N,
meanZ = mean(value, na.rm=TRUE),
sdZ = sd(value, na.rm=TRUE)),
.(Variable,
Label,
session)]
setkey(T, Variable, Label, session)Check against Table 2, column CT Group of Tager, FA, et al..
T1 <- T[session == "Post surgery before treatment"]
T1 <- T1[, x := sprintf("%.2f (%.2f)", meanZ, sdZ)]
T1[, .(Variable, Label, N, x)]## Variable Label N x
## 1: tapdomz Finger Tapper - Dom Hand 30 1.74 (1.21)
## 2: tapndomz Finger Tapper - NonDom Hand 30 1.38 (1.11)
## 3: pegdomz Pegboard - Dom Hand 30 -0.18 (1.67)
## 4: pegndomz Pegboard - Nondom Hand 30 -0.41 (1.64)
## 5: cowz COWAT 30 0.24 (0.94)
## 6: bntz Boston Naming 30 -0.33 (1.48)
## 7: trlaz Trail Making A 30 0.40 (1.00)
## 8: trlbz Trail Making B 30 0.32 (1.18)
## 9: dsymz WAIS-III Digit Symbol 30 0.69 (0.98)
## 10: dspaz WAIS-III Digit Span 30 0.23 (0.91)
## 11: aritz WAIS-III Arithmetic 30 0.09 (0.92)
## 12: numz WAIS-III Number/Letter 30 0.34 (0.90)
## 13: reyz Rey Copy 30 -1.52 (2.84)
## 14: bustotz Buschke Total 30 -0.60 (1.06)
## 15: bvrcoz Benton Visual Retention Correct 30 0.01 (1.17)Map session to months after treatment. Tager, FA, et al.
- Baseline
- 1 month after treatment
- 6 months after treatment
As far as I can tell, values of ptime map to these, although seemingly imprecise.
T <- T[session == "Post surgery before treatment", monthsPostTx := 0]
T <- T[session == "Post treatment/6mths post surgery", monthsPostTx := 6]
T <- T[session == "6 month follow-up", monthsPostTx := 12]Merge Kathleen’s KVanDyk@mednet.ucla.edu domain assignments.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Tuesday, November 03, 2015 5:08 PM
To: Ayse Tezcan
Cc: Benjamin Chan
Subject: RE: Cognitive impairment draft paper
Hi,
Attached is the Ono spreadsheet with a new column with my suggestions for
domains and domains for each Ahles test is in sheet 2. I've highlighted tests
that we may want to exclude if we want to consistently keep one or two
measures per test. Ben --- does it matter statistically if there is more than
one measure from the same test (for example delayed recall and delayed
recognition) in the same domain? In almost every case we have total and delay
for memory tests but if we add in more measures (Trial 6, Supraspan,
Recognition) does this confound analyses because these are likely highly
correlated measures within the same test? Would all of the studies need to
use the same measures in each test (i.e., every study uses Total and Delay)?
I might not be asking this clearly --- let me know what you think.f <- sprintf("%s/%s", pathIn, "Requested Chemo Data domains kvd 11.19.15 2.xlsx")
echoFile(f)## File: StudyDocuments/Requested Chemo Data domains kvd 11.19.15 2.xlsx
## Modification date: 2015-12-10 07:44:33
## File size: 178.9 KBD0 <- read.xlsx(f, sheet=1, check.names=TRUE)
D0 <- data.table(D0)
D0 <- D0[First.Auth == "Tager" & !is.na(DOMAIN.FOR.META..kvd.),
.(Label = Cog.Test,
CognitiveDomainForMetaAnalysis = DOMAIN.FOR.META..kvd.)]
D0 <- D0[Label == "WAIS-IIIDigit Span",
Label := "WAIS-III Digit Span"]
lookup <- D0
T <- merge(lookup, T, by="Label")
unique(T[, .(CognitiveDomainForMetaAnalysis, Label)])[order(CognitiveDomainForMetaAnalysis, Label)]## CognitiveDomainForMetaAnalysis Label
## 1: Attn/Wkg Mem/Concentration Trail Making A
## 2: Attn/Wkg Mem/Concentration WAIS-III Arithmetic
## 3: Attn/Wkg Mem/Concentration WAIS-III Digit Span
## 4: Attn/Wkg Mem/Concentration WAIS-III Number/Letter
## 5: Exec Fxn Trail Making B
## 6: Information Proc Speed WAIS-III Digit Symbol
## 7: Motor Speed Finger Tapper - Dom Hand
## 8: Motor Speed Finger Tapper - NonDom Hand
## 9: Motor Speed Pegboard - Dom Hand
## 10: Motor Speed Pegboard - Nondom Hand
## 11: Verbal Ability/Language Boston Naming
## 12: Verbal Ability/Language COWAT
## 13: Verbal Memory Buschke Total
## 14: Visuospatial Rey CopySave working data tables to file.
metadata <- makeMetadata(T)
f <- sprintf("%s/%s", pathOut, "Tager.RData")
save(T, metadata, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Output/Tager.RData saved on: 2016-01-13 09:35:02
## File size: 21.015 KB4.4 Reshape Ono
Reshape the Ono data set so it has a similar structure to the Ahles and Tager data.
f <- sprintf("%s/%s", pathOut, "Ono.RData")
load(f, verbose=TRUE)## Loading objects:
## D
## metadataD
## DFixed
## metadataDFixed
## DRandom
## metadataDRandommetadataD$timeStamp## [1] "2016-01-13 09:35:01 PST"metadataD$colNames## [1] "author" "comparisonGroup"
## [3] "healthyGroup" "treatmentGroup"
## [5] "timeDays" "nGroup1"
## [7] "nGroup2" "nTotal"
## [9] "ageGroup1" "ageGroup2"
## [11] "meanGroup1" "sdGroup1"
## [13] "meanGroup2" "sdGroup2"
## [15] "direction" "randomEffect"
## [17] "CognitiveDomainPrimary" "CogTest"
## [19] "CognitiveDomainForMetaAnalysis" "ScoreTyp"
## [21] "diffMean" "sdPooled"
## [23] "cohenD" "hedgesG"
## [25] "var1" "var2"
## [27] "variance" "se"
## [29] "weightFE" "effSizeWeightedFE"
## [31] "weightRE" "effSizeWeightedRE"Melt data.
idVars <- c("author",
"comparisonGroup",
"treatmentGroup",
"timeDays",
"CogTest",
"CognitiveDomainPrimary",
"CognitiveDomainForMetaAnalysis",
"ScoreTyp",
"ageGroup1")
DN <- melt(D, id.vars = idVars,
measure.vars = c("nGroup1", "nGroup2"), value.name = "N",
na.rm=TRUE)
DMean <- melt(D, id.vars = idVars,
measure.vars = c("meanGroup1", "meanGroup2"), value.name = "mean",
na.rm=TRUE)
DSD <- melt(D, id.vars = idVars,
measure.vars = c("sdGroup1", "sdGroup2"), value.name = "sd",
na.rm=TRUE)Check studies. Do not show
D[, .N, .(author, comparisonGroup, treatmentGroup, timeDays, ageGroup1)]Prepare measure data sets for merging.
DN <- DN [variable == "nGroup1" , group := "Group 1"]
DMean <- DMean[variable == "meanGroup1", group := "Group 1"]
DSD <- DSD [variable == "sdGroup1" , group := "Group 1"]
DN <- DN [variable == "nGroup2" , group := "Group 2"]
DMean <- DMean[variable == "meanGroup2", group := "Group 2"]
DSD <- DSD [variable == "sdGroup2" , group := "Group 2"]Merge the melted data.
setkeyv(DN, c(idVars, "group"))
setkeyv(DMean, c(idVars, "group"))
setkeyv(DSD, c(idVars, "group"))
D <- merge(DN[, variable := NULL], DMean[, variable := NULL])
D <- merge(D, DSD[, variable := NULL])Deduplicate pre-treatment data.
D1 <- D[group == "Group 1"]
setkeyv(D1, idVars[!(idVars %in% c("comparisonGroup", "treatmentGroup", "timeDays"))])
D1 <- unique(D1)
D1 <- D1[, monthsPostTx := 0]
D1 <- D1[,
`:=` (comparisonGroup = NULL,
treatmentGroup = NULL,
timeDays = NULL,
group = NULL)]Calculate monthsPostRx for post-treatment values.
D2 <- D[group == "Group 2"]
D2 <- D2[, monthsPostTx := round(timeDays / 365.25 * 12)]
D2 <- D2[,
`:=` (comparisonGroup = NULL,
timeDays = NULL,
group = NULL)]rbind pre-treatment and post-treatment data.
D <- rbind(D1, D2, fill=TRUE)Check data structure
unique(D[, .(author, monthsPostTx)])[order(author, monthsPostTx)]## author monthsPostTx
## 1: Bender 0
## 2: Bender 6
## 3: Bender 18
## 4: Collins 0
## 5: Collins 5
## 6: Collins 18
## 7: Jenkins 0
## 8: Jenkins 1
## 9: Jenkins 12
## 10: Wefel 0
## 11: Wefel 6
## 12: Wefel 18Rename the age variable.
setnames(D, "ageGroup1", "age")Overwrite the data to file.
metadata <- makeMetadata(D)
f <- sprintf("%s/%s", pathOut, "Ono.RData")
save(D, metadata, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Output/Ono.RData saved on: 2016-01-13 09:35:03
## File size: 22.25 KB4.5 Data abstraction
Create data sets from studies we had to abstract data from ourselves. I.e., data did not come from primary source authors or secondary source systematic reviews.
Structure of the data should be
authormonthsPostTxtreatmentGroupcognitiveDomainOriginal(remove this column since we won’t use it)cognitiveDomaincognitiveTestscoreTypenmeansd
4.5.1 Fan
Reference: Fan 2005, et al. J Clin Oncol. 2005 Nov 1;23(31):8025-32.
Data is in the Table 9.
There is a footnote to Table 9:
NOTE. Higher scores represent better function.
This is different from Trails A and B scores from other studies. Make sure to account for this different in combineData.Rmd.
Fan reports medians. We’ll assign their median values to the mean variable to keep things consistent.
Need to figure out what to do with sd
D <- rbind(data.table(monthsPostTx= 0, cognitiveTest="Trails A", mean=42.0, sd=NA, n=104, cognitiveDomain="Attn/Wkg Mem/Concentration"),
data.table(monthsPostTx= 0, cognitiveTest="Trails B", mean=47.5, sd=NA, n=104, cognitiveDomain="Exec Fxn"),
data.table(monthsPostTx=12, cognitiveTest="Trails A", mean=44.0, sd=NA, n= 91, cognitiveDomain="Attn/Wkg Mem/Concentration"),
data.table(monthsPostTx=12, cognitiveTest="Trails B", mean=49.0, sd=NA, n= 91, cognitiveDomain="Exec Fxn"),
data.table(monthsPostTx=24, cognitiveTest="Trails A", mean=47.0, sd=NA, n= 81, cognitiveDomain="Attn/Wkg Mem/Concentration"),
data.table(monthsPostTx=24, cognitiveTest="Trails B", mean=50.0, sd=NA, n= 81, cognitiveDomain="Exec Fxn"))
D <- D[,
`:=` (author = "Fan",
treatmentGroup = "Chemotherapy",
scoreType = "T score")]
D4a <- D
print(xtable(D4a), type="html")| monthsPostTx | cognitiveTest | mean | sd | n | cognitiveDomain | author | treatmentGroup | scoreType | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00 | Trails A | 42.00 | 104.00 | Attn/Wkg Mem/Concentration | Fan | Chemotherapy | T score | |
| 2 | 0.00 | Trails B | 47.50 | 104.00 | Exec Fxn | Fan | Chemotherapy | T score | |
| 3 | 12.00 | Trails A | 44.00 | 91.00 | Attn/Wkg Mem/Concentration | Fan | Chemotherapy | T score | |
| 4 | 12.00 | Trails B | 49.00 | 91.00 | Exec Fxn | Fan | Chemotherapy | T score | |
| 5 | 24.00 | Trails A | 47.00 | 81.00 | Attn/Wkg Mem/Concentration | Fan | Chemotherapy | T score | |
| 6 | 24.00 | Trails B | 50.00 | 81.00 | Exec Fxn | Fan | Chemotherapy | T score |
4.5.2 McDonald
Reference: McDonald 2012, et al. J Clin Oncol. 2012 Jul 10;30(20):2500-8. doi: 10.1200/JCO.2011.38.5674. Epub 2012 Jun 4
Data is in the Table A2 of the appendix.
D <- rbind(data.table(monthsPostTx= 0, cognitiveTest="0-back", mean=96.1, sd= 8.2),
data.table(monthsPostTx= 0, cognitiveTest="1-back", mean=78.3, sd=30.1),
data.table(monthsPostTx= 0, cognitiveTest="2-back", mean=80.1, sd=27.5),
data.table(monthsPostTx= 0, cognitiveTest="3-back", mean=66.7, sd=25.2),
data.table(monthsPostTx=12, cognitiveTest="0-back", mean=88.0, sd=21.9),
data.table(monthsPostTx=12, cognitiveTest="1-back", mean=89.1, sd=13.2),
data.table(monthsPostTx=12, cognitiveTest="2-back", mean=83.1, sd=16.1),
data.table(monthsPostTx=12, cognitiveTest="3-back", mean=68.4, sd=22.9))
D <- D[,
`:=` (author = "McDonald",
treatmentGroup = "CTx+",
cognitiveDomain = "Attn/Wkg Mem/Concentration",
scoreType = "% accuracy",
n = 16)]
D4b <- D
print(xtable(D4b), type="html")| monthsPostTx | cognitiveTest | mean | sd | author | treatmentGroup | cognitiveDomain | scoreType | n | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00 | 0-back | 96.10 | 8.20 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
| 2 | 0.00 | 1-back | 78.30 | 30.10 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
| 3 | 0.00 | 2-back | 80.10 | 27.50 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
| 4 | 0.00 | 3-back | 66.70 | 25.20 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
| 5 | 12.00 | 0-back | 88.00 | 21.90 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
| 6 | 12.00 | 1-back | 89.10 | 13.20 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
| 7 | 12.00 | 2-back | 83.10 | 16.10 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
| 8 | 12.00 | 3-back | 68.40 | 22.90 | McDonald | CTx+ | Attn/Wkg Mem/Concentration | % accuracy | 16.00 |
4.5.3 Wefel 2010
Reference: Wefel 2010, et al Cancer. 2010 Jul 15;116(14):3348-56. doi: 10.1002/cncr.25098.
Data is in the Table 4. Use data from Time Point 1 and Time Point 4.
Cognitive testing was conducted at baseline [Time Point 1] and then on average 2.9 months (standard deviation “SD” = 0.59), 7.0 months (SD, 1.4), and 13.1 months (SD = 2.8) after baseline [Time Point 4].
D <- rbind(data.table(monthsPostTx= 0 , n=42, mean= 0.09, sd=0.86, cognitiveTest="Hopkins Verbal Learning Test Total", cognitiveDomain="Verbal Memory", scoreType="Z-score"),
data.table(monthsPostTx= 0 , n=42, mean=-0.18, sd=2.51, cognitiveTest="Trail Making Part B", cognitiveDomain="Exec Fxn", scoreType="Z-score"),
data.table(monthsPostTx= 0 , n=42, mean= 0.29, sd=0.96, cognitiveTest="MAE Controlled Oral Word Association", cognitiveDomain="Verbal Ability/Language", scoreType="Z-score"),
data.table(monthsPostTx= 0 , n=42, mean=-0.34, sd=1.57, cognitiveTest="Trail Making Part A", cognitiveDomain="Attn/Wkg Mem/Concentration", scoreType="Z-score"),
data.table(monthsPostTx= 0 , n=42, mean=11.71, sd=2.45, cognitiveTest="WAIS-R Digit Symbol", cognitiveDomain="Information Proc Speed", scoreType="Scaled score"),
data.table(monthsPostTx= 0 , n=42, mean= 9.00, sd=2.48, cognitiveTest="WAIS-R Digit Span", cognitiveDomain="Attn/Wkg Mem/Concentration", scoreType="Scaled score"),
data.table(monthsPostTx=13.1, n=28, mean=-0.44, sd=1.23, cognitiveTest="Hopkins Verbal Learning Test Total", cognitiveDomain="Verbal Memory", scoreType="Z-score"),
data.table(monthsPostTx=13.1, n=28, mean= 0.15, sd=1.22, cognitiveTest="Trail Making Part B", cognitiveDomain="Exec Fxn", scoreType="Z-score"),
data.table(monthsPostTx=13.1, n=28, mean= 0.30, sd=1.15, cognitiveTest="MAE Controlled Oral Word Association", cognitiveDomain="Verbal Ability/Language", scoreType="Z-score"),
data.table(monthsPostTx=13.1, n=28, mean= 0.56, sd=1.29, cognitiveTest="Trail Making Part A", cognitiveDomain="Attn/Wkg Mem/Concentration", scoreType="Z-score"),
data.table(monthsPostTx=13.1, n=28, mean=13.25, sd=2.03, cognitiveTest="WAIS-R Digit Symbol", cognitiveDomain="Information Proc Speed", scoreType="Scaled score"),
data.table(monthsPostTx=13.1, n=28, mean=10.04, sd=2.67, cognitiveTest="WAIS-R Digit Span", cognitiveDomain="Attn/Wkg Mem/Concentration", scoreType="Scaled score"))
D <- D[,
`:=` (author = "Wefel 2010",
treatmentGroup = "Chemotherapy with or without paclitaxel")]
D4c <- D
print(xtable(D4c), type="html")| monthsPostTx | n | mean | sd | cognitiveTest | cognitiveDomain | scoreType | author | treatmentGroup | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00 | 42.00 | 0.09 | 0.86 | Hopkins Verbal Learning Test Total | Verbal Memory | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 2 | 0.00 | 42.00 | -0.18 | 2.51 | Trail Making Part B | Exec Fxn | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 3 | 0.00 | 42.00 | 0.29 | 0.96 | MAE Controlled Oral Word Association | Verbal Ability/Language | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 4 | 0.00 | 42.00 | -0.34 | 1.57 | Trail Making Part A | Attn/Wkg Mem/Concentration | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 5 | 0.00 | 42.00 | 11.71 | 2.45 | WAIS-R Digit Symbol | Information Proc Speed | Scaled score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 6 | 0.00 | 42.00 | 9.00 | 2.48 | WAIS-R Digit Span | Attn/Wkg Mem/Concentration | Scaled score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 7 | 13.10 | 28.00 | -0.44 | 1.23 | Hopkins Verbal Learning Test Total | Verbal Memory | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 8 | 13.10 | 28.00 | 0.15 | 1.22 | Trail Making Part B | Exec Fxn | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 9 | 13.10 | 28.00 | 0.30 | 1.15 | MAE Controlled Oral Word Association | Verbal Ability/Language | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 10 | 13.10 | 28.00 | 0.56 | 1.29 | Trail Making Part A | Attn/Wkg Mem/Concentration | Z-score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 11 | 13.10 | 28.00 | 13.25 | 2.03 | WAIS-R Digit Symbol | Information Proc Speed | Scaled score | Wefel 2010 | Chemotherapy with or without paclitaxel |
| 12 | 13.10 | 28.00 | 10.04 | 2.67 | WAIS-R Digit Span | Attn/Wkg Mem/Concentration | Scaled score | Wefel 2010 | Chemotherapy with or without paclitaxel |
4.5.4 Dumas
Reference: Dumas 2013, et al. Brain Imaging Behav. 2013 Dec; 7(4): 10.1007/s11682-013-9244-1.
Data is in the Table 3.
D <- rbind(data.table(monthsPostTx= 0, cognitiveTest="0-back sensitivity ", mean=2.27, sd=0.9),
data.table(monthsPostTx= 0, cognitiveTest="1-back sensitivity ", mean=1.70, sd=1.2),
data.table(monthsPostTx= 0, cognitiveTest="2-back sensitivity ", mean=1.89, sd=0.8),
data.table(monthsPostTx= 0, cognitiveTest="3-back sensitivity ", mean=1.32, sd=0.9),
data.table(monthsPostTx=12, cognitiveTest="0-back sensitivity ", mean=2.40, sd=0.8),
data.table(monthsPostTx=12, cognitiveTest="1-back sensitivity ", mean=2.29, sd=1.0),
data.table(monthsPostTx=12, cognitiveTest="2-back sensitivity ", mean=1.68, sd=0.7),
data.table(monthsPostTx=12, cognitiveTest="3-back sensitivity ", mean=1.60, sd=0.8),
data.table(monthsPostTx= 0, cognitiveTest="0-back bias ", mean=0.20, sd=0.3),
data.table(monthsPostTx= 0, cognitiveTest="1-back bias ", mean=0.22, sd=0.5),
data.table(monthsPostTx= 0, cognitiveTest="2-back bias ", mean=0.05, sd=0.4),
data.table(monthsPostTx= 0, cognitiveTest="3-back bias ", mean=0.61, sd=0.3),
data.table(monthsPostTx=12, cognitiveTest="0-back bias ", mean=0.26, sd=0.3),
data.table(monthsPostTx=12, cognitiveTest="1-back bias ", mean=0.06, sd=0.2),
data.table(monthsPostTx=12, cognitiveTest="2-back bias ", mean=0.03, sd=.03),
data.table(monthsPostTx=12, cognitiveTest="3-back bias ", mean=0.32, sd=0.2))
D <- D[,
`:=` (author = "Dumas",
treatmentGroup = "Chemotherapy +",
cognitiveDomain = "Attn/Wkg Mem/Concentration",
scoreType = "",
n = 9)]
D4d <- D
print(xtable(D4d), type="html")| monthsPostTx | cognitiveTest | mean | sd | author | treatmentGroup | cognitiveDomain | scoreType | n | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00 | 0-back sensitivity | 2.27 | 0.90 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 2 | 0.00 | 1-back sensitivity | 1.70 | 1.20 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 3 | 0.00 | 2-back sensitivity | 1.89 | 0.80 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 4 | 0.00 | 3-back sensitivity | 1.32 | 0.90 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 5 | 12.00 | 0-back sensitivity | 2.40 | 0.80 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 6 | 12.00 | 1-back sensitivity | 2.29 | 1.00 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 7 | 12.00 | 2-back sensitivity | 1.68 | 0.70 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 8 | 12.00 | 3-back sensitivity | 1.60 | 0.80 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 9 | 0.00 | 0-back bias | 0.20 | 0.30 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 10 | 0.00 | 1-back bias | 0.22 | 0.50 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 11 | 0.00 | 2-back bias | 0.05 | 0.40 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 12 | 0.00 | 3-back bias | 0.61 | 0.30 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 13 | 12.00 | 0-back bias | 0.26 | 0.30 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 14 | 12.00 | 1-back bias | 0.06 | 0.20 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 15 | 12.00 | 2-back bias | 0.03 | 0.03 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 | |
| 16 | 12.00 | 3-back bias | 0.32 | 0.20 | Dumas | Chemotherapy + | Attn/Wkg Mem/Concentration | 9.00 |
4.5.5 Moore
Reference: Moore 2014, et al. Support Care Cancer. 2014 Aug;22(8):2127-31. doi: 10.1007/s00520-014-2197-0. Epub 2014 Mar 20.
Data was sent by the study author.
D <- rbind(data.table(monthsPostTx= 0, cognitiveTest="WAIS-III Symbol Search" , mean=29, sd= 6),
data.table(monthsPostTx= 0, cognitiveTest="WAIS-III Digit Symbol Coding", mean=75, sd=14),
data.table(monthsPostTx= 1, cognitiveTest="WAIS-III Symbol Search" , mean=32, sd= 4),
data.table(monthsPostTx= 1, cognitiveTest="WAIS-III Digit Symbol Coding", mean=74, sd= 8),
data.table(monthsPostTx=12, cognitiveTest="WAIS-III Symbol Search" , mean=33, sd= 4),
data.table(monthsPostTx=12, cognitiveTest="WAIS-III Digit Symbol Coding", mean=74, sd=14))
D <- D[,
`:=` (author = "Moore",
treatmentGroup = "Chemotherapy",
cognitiveDomain = "Info Proc Speed",
scoreType = "",
n = 7)]
D4e <- D
print(xtable(D4e), type="html")| monthsPostTx | cognitiveTest | mean | sd | author | treatmentGroup | cognitiveDomain | scoreType | n | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00 | WAIS-III Symbol Search | 29.00 | 6.00 | Moore | Chemotherapy | Info Proc Speed | 7.00 | |
| 2 | 0.00 | WAIS-III Digit Symbol Coding | 75.00 | 14.00 | Moore | Chemotherapy | Info Proc Speed | 7.00 | |
| 3 | 1.00 | WAIS-III Symbol Search | 32.00 | 4.00 | Moore | Chemotherapy | Info Proc Speed | 7.00 | |
| 4 | 1.00 | WAIS-III Digit Symbol Coding | 74.00 | 8.00 | Moore | Chemotherapy | Info Proc Speed | 7.00 | |
| 5 | 12.00 | WAIS-III Symbol Search | 33.00 | 4.00 | Moore | Chemotherapy | Info Proc Speed | 7.00 | |
| 6 | 12.00 | WAIS-III Digit Symbol Coding | 74.00 | 14.00 | Moore | Chemotherapy | Info Proc Speed | 7.00 |
4.6 Combine data from studies
Load data from
- Ono
- Ahles
- Tager
f <- sprintf("%s/%s", pathOut, "Ono.RData")
load(f, verbose=TRUE)## Loading objects:
## D
## metadatametadata$colNames## [1] "author" "CogTest"
## [3] "CognitiveDomainPrimary" "CognitiveDomainForMetaAnalysis"
## [5] "ScoreTyp" "age"
## [7] "N" "mean"
## [9] "sd" "monthsPostTx"
## [11] "treatmentGroup"D1 <- D
colNames1 <- metadataD$colNames
f <- sprintf("%s/%s", pathOut, "Ahles.RData")
load(f, verbose=TRUE)## Loading objects:
## D
## metadatametadata$colNames## [1] "Variable" "CognitiveDomainForMetaAnalysis"
## [3] "txgrp" "ptime"
## [5] "NObs" "Label"
## [7] "N" "Mean"
## [9] "Median" "StdDev"
## [11] "monthsPostTx"D2 <- D
f <- sprintf("%s/%s", pathOut, "Tager.RData")
load(f, verbose=TRUE)## Loading objects:
## T
## metadatametadata$colNames## [1] "Label" "CognitiveDomainForMetaAnalysis"
## [3] "Variable" "session"
## [5] "N" "meanZ"
## [7] "sdZ" "monthsPostTx"D3 <- TStructure of the data should be
authormonthsPostTxtreatmentGroupcognitiveDomainOriginal(remove this column since we won’t use it)cognitiveDomaincognitiveTestscoreTypenmeansd
Restructure Ono.
colOrder <- c("author",
"monthsPostTx",
"treatmentGroup",
"cognitiveDomain",
"cognitiveTest",
"scoreType",
"n",
"mean",
"sd")
setnames(D1,
c("CogTest", "CognitiveDomainPrimary", "CognitiveDomainForMetaAnalysis", "ScoreTyp", "N"),
c("cognitiveTest", "cognitiveDomainOriginal", "cognitiveDomain", "scoreType", "n"))
D1 <- D1[author == "Wefel", author := "Wefel 2004"]
D1 <- D1[scoreType == "z score", scoreType := "Z-score"]
D1 <- D1[scoreType == "Scaled scores", scoreType := "Scaled score"]
D1[,
`:=` (cognitiveDomainOriginal = NULL)]
setcolorder(D1, c(colOrder, "age"))Fix a data errors in Collins. Remove a duplicate row.
n0 <- nrow(D1)
D1 <- D1[!(author == "Collins" & monthsPostTx == 0 &
cognitiveTest %in% c("Letter-number sequencing: WAIS-III", "Symbol search: WAIS-III"))]
message(sprintf("Removed %d rows", n0 - nrow(D1)))## Removed 2 rowsRestructure Ahles.
D2 <- D2[, author := "Ahles"]
setnames(D2,
c("CognitiveDomainForMetaAnalysis", "txgrp", "Label", "N", "Mean", "StdDev"),
c("cognitiveDomain", "treatmentGroup", "cognitiveTest", "n", "mean", "sd"))
D2[,
`:=` (Variable = NULL,
ptime = NULL,
NObs = NULL,
Median = NULL,
scoreType = "Z-score")]
setcolorder(D2, colOrder)Restructure Tager.
D3 <- D3[, author := "Tager"]
setnames(D3,
c("Label", "CognitiveDomainForMetaAnalysis", "N", "meanZ", "sdZ"),
c("cognitiveTest", "cognitiveDomain", "n", "mean", "sd"))
D3[,
`:=` (Variable = NULL,
session = NULL,
treatmentGroup = "Chemo",
scoreType = "Z-score")]
setcolorder(D3, colOrder)rbindlist the data.
D <- rbindlist(list(D1, D2, D3, D4a, D4b, D4c, D4d, D4e), use.names=TRUE, fill=TRUE)Fill in age for these studies.
unique(D[is.na(age), author])## [1] "Ahles" "Tager" "Fan" "McDonald" "Wefel 2010"
## [6] "Dumas" "Moore"D <- D[author == "Ahles" , age := 51.7]
D <- D[author == "Tager" , age := 60.3]
D <- D[author == "Fan" , age := 48]
D <- D[author == "McDonald" , age := 52.9]
D <- D[author == "Wefel 2010", age := 48.8]
D <- D[author == "Dumas" , age := 57.1]
D <- D[author == "Moore" , age := 53]Standardize age.
age <- unique(D[, .(author, age)])
age <- age[, ageCentered := scale(age, center=TRUE, scale=FALSE)]
age[,
.(meanRaw = mean(age),
sdRaw = sd(age),
meanCentered = mean(ageCentered),
sdCentered = sd(ageCentered))]## meanRaw sdRaw meanCentered sdCentered
## 1: 50.9025 5.958379 -3.552714e-15 5.958379age[order(age)]## author age ageCentered
## 1: Bender 40.11 -10.7925
## 2: Bender 44.13 -6.7725
## 3: Wefel 2004 45.40 -5.5025
## 4: Fan 48.00 -2.9025
## 5: Wefel 2010 48.80 -2.1025
## 6: Jenkins 51.49 0.5875
## 7: Ahles 51.70 0.7975
## 8: McDonald 52.90 1.9975
## 9: Moore 53.00 2.0975
## 10: Dumas 57.10 6.1975
## 11: Collins 57.90 6.9975
## 12: Tager 60.30 9.3975D <- merge(D, age, by=c("author", "age"))Identify tests where higher values are worse.
D <- D[,
isHigherWorse :=
(grepl("sec|time", cognitiveTest, ignore.case=TRUE) &
!(grepl("4WSTM", cognitiveTest) | grepl("PASAT", cognitiveTest))) |
grepl("pegboard", cognitiveTest, ignore.case=TRUE) |
(grepl("stroop", cognitiveTest, ignore.case=TRUE) & !grepl("^stroop$", cognitiveTest, ignore.case=TRUE))|
(grepl("trail", cognitiveTest, ignore.case=TRUE) & author != "Fan")]
tests <- unique(D[, .(isHigherWorse, cognitiveDomain, cognitiveTest)])
setorder(tests, isHigherWorse, cognitiveDomain, cognitiveTest)
print(xtable(tests[isHigherWorse == TRUE]), type="html")| isHigherWorse | cognitiveDomain | cognitiveTest | |
|---|---|---|---|
| 1 | TRUE | Attn/Wkg Mem/Concentration | CPT: Distractibility, Reaction Time |
| 2 | TRUE | Attn/Wkg Mem/Concentration | CPT: Vigilance, Reaction Time |
| 3 | TRUE | Attn/Wkg Mem/Concentration | DKEFS Trails: Letter Sequencing, sec |
| 4 | TRUE | Attn/Wkg Mem/Concentration | DKEFS Trails: Number Sequencing, sec |
| 5 | TRUE | Attn/Wkg Mem/Concentration | TMT part A time |
| 6 | TRUE | Attn/Wkg Mem/Concentration | Trail Making A |
| 7 | TRUE | Attn/Wkg Mem/Concentration | Trail Making Part A |
| 8 | TRUE | Attn/Wkg Mem/Concentration | Trails A |
| 9 | TRUE | Exec Fxn | DKEFS Stroop: Color-Word |
| 10 | TRUE | Exec Fxn | DKEFS Trails: Number-Letter Switching, sec |
| 11 | TRUE | Exec Fxn | DKEFS: Stroop, Set Shifting |
| 12 | TRUE | Exec Fxn | TMT part B time |
| 13 | TRUE | Exec Fxn | Trail Making B |
| 14 | TRUE | Exec Fxn | Trail Making Part B |
| 15 | TRUE | Exec Fxn | Trails B |
| 16 | TRUE | Information Proc Speed | DKEFS Stroop: Color Patch Naming |
| 17 | TRUE | Information Proc Speed | DKEFS Stroop: Word Reading, sec |
| 18 | TRUE | Information Proc Speed | DKEFS Trails: Visual Scanning in Seconds |
| 19 | TRUE | Motor Speed | DKEFS Trails: Motor Speed, sec |
| 20 | TRUE | Motor Speed | Grooved Peg Board time |
| 21 | TRUE | Motor Speed | Grooved Pegboard Test: Left Hand, sec |
| 22 | TRUE | Motor Speed | Grooved Pegboard Test: Right Hand, sec |
| 23 | TRUE | Motor Speed | Grooved pegboard dominant hand |
| 24 | TRUE | Motor Speed | Grooved pegboard nondominant hand |
| 25 | TRUE | Motor Speed | Pegboard - Dom Hand |
| 26 | TRUE | Motor Speed | Pegboard - Nondom Hand |
print(xtable(tests[isHigherWorse == FALSE]), type="html")| isHigherWorse | cognitiveDomain | cognitiveTest | |
|---|---|---|---|
| 1 | FALSE | Attn/Wkg Mem/Concentration | WAIS-III -Arithmetic |
| 2 | FALSE | Attn/Wkg Mem/Concentration | 0-back |
| 3 | FALSE | Attn/Wkg Mem/Concentration | 0-back bias |
| 4 | FALSE | Attn/Wkg Mem/Concentration | 0-back sensitivity |
| 5 | FALSE | Attn/Wkg Mem/Concentration | 1-back |
| 6 | FALSE | Attn/Wkg Mem/Concentration | 1-back bias |
| 7 | FALSE | Attn/Wkg Mem/Concentration | 1-back sensitivity |
| 8 | FALSE | Attn/Wkg Mem/Concentration | 2-back |
| 9 | FALSE | Attn/Wkg Mem/Concentration | 2-back bias |
| 10 | FALSE | Attn/Wkg Mem/Concentration | 2-back sensitivity |
| 11 | FALSE | Attn/Wkg Mem/Concentration | 3-back |
| 12 | FALSE | Attn/Wkg Mem/Concentration | 3-back bias |
| 13 | FALSE | Attn/Wkg Mem/Concentration | 3-back sensitivity |
| 14 | FALSE | Attn/Wkg Mem/Concentration | 4WSTM 15 sec |
| 15 | FALSE | Attn/Wkg Mem/Concentration | 4WSTM 30 sec |
| 16 | FALSE | Attn/Wkg Mem/Concentration | 4WSTM 5 sec |
| 17 | FALSE | Attn/Wkg Mem/Concentration | CPT: Distractibility, Correct Responses |
| 18 | FALSE | Attn/Wkg Mem/Concentration | CPT: Distractibility, False Positives |
| 19 | FALSE | Attn/Wkg Mem/Concentration | CPT: Vigilance, Correct Responses |
| 20 | FALSE | Attn/Wkg Mem/Concentration | CPT: Vigilance, False Positives |
| 21 | FALSE | Attn/Wkg Mem/Concentration | Consonant trigrams |
| 22 | FALSE | Attn/Wkg Mem/Concentration | Letter-number sequencing: WAIS-III |
| 23 | FALSE | Attn/Wkg Mem/Concentration | PASAT (Rao): 2 second pacing |
| 24 | FALSE | Attn/Wkg Mem/Concentration | PASAT (Rao): 3 second pacing |
| 25 | FALSE | Attn/Wkg Mem/Concentration | PASAT number correct |
| 26 | FALSE | Attn/Wkg Mem/Concentration | Spatial span: WMS-III |
| 27 | FALSE | Attn/Wkg Mem/Concentration | Trails A |
| 28 | FALSE | Attn/Wkg Mem/Concentration | WAIS-III Arithmetic |
| 29 | FALSE | Attn/Wkg Mem/Concentration | WAIS-III Digit Span |
| 30 | FALSE | Attn/Wkg Mem/Concentration | WAIS-III Digit span |
| 31 | FALSE | Attn/Wkg Mem/Concentration | WAIS-III Letter-number sequencing |
| 32 | FALSE | Attn/Wkg Mem/Concentration | WAIS-III Number/Letter |
| 33 | FALSE | Attn/Wkg Mem/Concentration | WAIS-R Digit Span |
| 34 | FALSE | Attn/Wkg Mem/Concentration | WAIS-R arithmetic |
| 35 | FALSE | Attn/Wkg Mem/Concentration | WAIS-R digit span |
| 36 | FALSE | Attn/Wkg Mem/Concentration | WMS-III digit span backwards |
| 37 | FALSE | Attn/Wkg Mem/Concentration | WMS-III digit span forward |
| 38 | FALSE | Attn/Wkg Mem/Concentration | WMS-III letter number sequencing |
| 39 | FALSE | Attn/Wkg Mem/Concentration | WMS-III spatial span backwards |
| 40 | FALSE | Attn/Wkg Mem/Concentration | WMS-III spatial span forwards |
| 41 | FALSE | Exec Fxn | DKEFS Card Sorting: Confirmed Correct Sorts |
| 42 | FALSE | Exec Fxn | DKEFS Card Sorting: Free Sorting |
| 43 | FALSE | Exec Fxn | DKEFS Verbal Fluency: Switching Fruits/Veget |
| 44 | FALSE | Exec Fxn | DKEFS: Card Sorting, Sort Recognition |
| 45 | FALSE | Exec Fxn | Stroop |
| 46 | FALSE | Exec Fxn | Trails B |
| 47 | FALSE | Exec Fxn | WAIS-R similarities |
| 48 | FALSE | Exec Fxn | WCST sorts divided by trials |
| 49 | FALSE | Info Proc Speed | WAIS-III Digit Symbol Coding |
| 50 | FALSE | Info Proc Speed | WAIS-III Symbol Search |
| 51 | FALSE | Information Proc Speed | CVLT-2: Digit Symbol |
| 52 | FALSE | Information Proc Speed | Letter cancellation |
| 53 | FALSE | Information Proc Speed | Symbol search: WAIS-III |
| 54 | FALSE | Information Proc Speed | WAIS-III Digit Symbol |
| 55 | FALSE | Information Proc Speed | WAIS-III Digit Symbol Coding |
| 56 | FALSE | Information Proc Speed | WAIS-III Symbol search |
| 57 | FALSE | Information Proc Speed | WAIS-R Digit Symbol |
| 58 | FALSE | Information Proc Speed | WAIS-R digit symbol |
| 59 | FALSE | Motor Speed | Finger Tapper - Dom Hand |
| 60 | FALSE | Motor Speed | Finger Tapper - NonDom Hand |
| 61 | FALSE | Verbal Ability/Language | Boston Naming |
| 62 | FALSE | Verbal Ability/Language | Boston Naming Test number correct |
| 63 | FALSE | Verbal Ability/Language | COWAT |
| 64 | FALSE | Verbal Ability/Language | DKEFS Verbal Fluency |
| 65 | FALSE | Verbal Ability/Language | DKEFS Verbal Fluency: anival or clothing and names |
| 66 | FALSE | Verbal Ability/Language | MAE Controlled Oral Word Association |
| 67 | FALSE | Verbal Ability/Language | Verbal Fluency FAS number correct |
| 68 | FALSE | Verbal Ability/Language | Verbal fluency COWAT correct |
| 69 | FALSE | Verbal Ability/Language | WASI: Vocabulary |
| 70 | FALSE | Verbal Ability/Language | WRAT-3 Reading Score |
| 71 | FALSE | Verbal Memory | AVLT delayed |
| 72 | FALSE | Verbal Memory | AVLT total |
| 73 | FALSE | Verbal Memory | Buschke Total |
| 74 | FALSE | Verbal Memory | CVLT delayed recall |
| 75 | FALSE | Verbal Memory | CVLT delayed recognition |
| 76 | FALSE | Verbal Memory | CVLT-2: Long Delay Free Recall |
| 77 | FALSE | Verbal Memory | CVLT-2: Trials 1-5 Total |
| 78 | FALSE | Verbal Memory | Hopkins Verbal Learning Test Total |
| 79 | FALSE | Verbal Memory | RAVL delayed recall |
| 80 | FALSE | Verbal Memory | RAVL total score |
| 81 | FALSE | Verbal Memory | WMS-III Logical memory II |
| 82 | FALSE | Verbal Memory | WMS-III Story delayed recall |
| 83 | FALSE | Verbal Memory | WMS-III Story immediate recall |
| 84 | FALSE | Verbal Memory | Wechsler Memory Scale-3: Logical Memory I |
| 85 | FALSE | Verbal Memory | Wechsler Memory Scale-3: Logical Memory II |
| 86 | FALSE | Visual Memory | Complex figure delayed |
| 87 | FALSE | Visual Memory | Complex figure immediate |
| 88 | FALSE | Visual Memory | RCF delayed recall |
| 89 | FALSE | Visual Memory | RCF immediate recall |
| 90 | FALSE | Visual Memory | RVLT delayed recall |
| 91 | FALSE | Visual Memory | RVLT delayed recognition |
| 92 | FALSE | Visual Memory | WMS-III Family pictures II |
| 93 | FALSE | Visual Memory | Wechsler Memory Scale-3: Faces I |
| 94 | FALSE | Visual Memory | Wechsler Memory Scale-3: Faces II |
| 95 | FALSE | Visuospatial | Rey Copy |
| 96 | FALSE | Visuospatial | WAIS-III Block design |
| 97 | FALSE | Visuospatial | WAIS-R block design |
| 98 | FALSE | Visuospatial | WASI: Block Design |
Output to CSV for Kathleen to verify. No longer needed. See issue #7.
f <- "tests.csv"
write.csv(tests, f, row.names=FALSE)Shorten domain labels.
setnames(D, c("cognitiveDomain", "cognitiveTest"), c("domain", "test"))
D <- D[domain == "Attn/Wkg Mem/Concentration", domain := "Attn/Wkg Mem/Concen"]
D <- D[domain == "Verbal Memory", domain := "Verb Mem"]
D <- D[domain == "Visual Memory", domain := "Vis Mem"]
D <- D[domain == "Verbal Ability/Language", domain := "Verb Ability/Lang"]
# D <- D[domain == "Motor Speed", domain := ""]
D <- D[domain == "Information Proc Speed", domain := "Info Proc Speed"]
# D <- D[domain == "Exec Fxn", domain := ""]
# D <- D[domain == "Visuospatial", domain := ""]
D <- D[, domain := factor(domain)]
D[, .N, domain]## domain N
## 1: Verb Ability/Lang 33
## 2: Visuospatial 13
## 3: Verb Mem 54
## 4: Exec Fxn 51
## 5: Info Proc Speed 39
## 6: Attn/Wkg Mem/Concen 146
## 7: Motor Speed 33
## 8: Vis Mem 35Trim leading and trailing whitespace.
D <- D[, test := gsub("^[[:space:]]*", "", test)]
D <- D[, test := gsub("[[:space:]]*$", "", test)]Standardize test labels.
D <- D[, test := gsub(":", "", test)]
D <- D[, test := gsub("anival", "animal", test)]
D <- D[, test := gsub("Wechsler Memory Scale-3", "WMS-III", test)]
D <- D[, test := gsub("Trails A", "TMT A", test)]
D <- D[, test := gsub("Trail Making A", "TMT A", test)]
D <- D[, test := gsub("Trail Making Part A", "TMT A", test)]
D <- D[, test := gsub("TMT part A time", "TMT A", test)]
D <- D[, test := gsub("Trails B", "TMT B", test)]
D <- D[, test := gsub("Trail Making B", "TMT B", test)]
D <- D[, test := gsub("Trail Making Part B", "TMT B", test)]
D <- D[, test := gsub("TMT part B time", "TMT B", test)]
D <- D[, test := gsub(" in Seconds", ", sec", test)]
D <- D[, test := gsub("second", "sec", test)]
D <- D[, test := gsub(" - ", " ", test)]
D <- D[, test := gsub("WAIS-III -Arithmetic", "WAIS-III Arithmetic", test)]
D <- D[, test := gsub("WAIS-III Letter-number sequencing", "WAIS-III Letter-number", test)]
D <- D[, test := gsub("WAIS-III Number/Letter", "WAIS-III Letter-number", test)]
D <- D[, test := gsub("Spatial span WMS-III", "WMS-III Spatial span", test)]
D <- D[, test := gsub("Verbal fluency COWAT correct", "COWAT Verbal fluency correct", test)]
D <- D[, test := gsub("^Boston Naming Test number correct$", "Boston Naming Test", test)]
D <- D[, test := gsub("^Boston Naming$", "Boston Naming Test", test)]
D <- D[, test := gsub("Peg Board time", "Pegboard, sec", test)]
D <- D[, test := gsub("^Pegboard", "Grooved Pegboard", test)]
D <- D[, test := gsub("dominant hand", "Dom Hand", test)]
D <- D[, test := gsub("nondominant hand", "Nondom Hand", test)]
D <- D[, test := gsub("nonDom", "Nondom", test)]
D <- D[, test := gsub("Grooved Pegboard Test", "Grooved Pegboard", test)]
D <- D[, test := toTitleCase(test)]
D <- D[, test := gsub("4wstm", "4WSTM", test)]
D <- D[, test := gsub("TMT a", "TMT A", test)]
unique(D[, test])[order(unique(D[, test]))]## [1] "0-Back"
## [2] "0-Back Bias"
## [3] "0-Back Sensitivity"
## [4] "1-Back"
## [5] "1-Back Bias"
## [6] "1-Back Sensitivity"
## [7] "2-Back"
## [8] "2-Back Bias"
## [9] "2-Back Sensitivity"
## [10] "3-Back"
## [11] "3-Back Bias"
## [12] "3-Back Sensitivity"
## [13] "4WSTM 15 Sec"
## [14] "4WSTM 30 Sec"
## [15] "4WSTM 5 Sec"
## [16] "AVLT Delayed"
## [17] "AVLT Total"
## [18] "Boston Naming Test"
## [19] "Buschke Total"
## [20] "Complex Figure Delayed"
## [21] "Complex Figure Immediate"
## [22] "Consonant Trigrams"
## [23] "COWAT"
## [24] "COWAT Verbal Fluency Correct"
## [25] "CPT Distractibility, Correct Responses"
## [26] "CPT Distractibility, False Positives"
## [27] "CPT Distractibility, Reaction Time"
## [28] "CPT Vigilance, Correct Responses"
## [29] "CPT Vigilance, False Positives"
## [30] "CPT Vigilance, Reaction Time"
## [31] "CVLT-2 Digit Symbol"
## [32] "CVLT-2 Long Delay Free Recall"
## [33] "CVLT-2 Trials 1-5 Total"
## [34] "CVLT Delayed Recall"
## [35] "CVLT Delayed Recognition"
## [36] "DKEFS Card Sorting Confirmed Correct Sorts"
## [37] "DKEFS Card Sorting Free Sorting"
## [38] "DKEFS Card Sorting, Sort Recognition"
## [39] "DKEFS Stroop Color-Word"
## [40] "DKEFS Stroop Color Patch Naming"
## [41] "DKEFS Stroop Word Reading, Sec"
## [42] "DKEFS Stroop, Set Shifting"
## [43] "DKEFS Trails Letter Sequencing, Sec"
## [44] "DKEFS Trails Motor Speed, Sec"
## [45] "DKEFS Trails Number-Letter Switching, Sec"
## [46] "DKEFS Trails Number Sequencing, Sec"
## [47] "DKEFS Trails Visual Scanning, Sec"
## [48] "DKEFS Verbal Fluency"
## [49] "DKEFS Verbal Fluency Animal or Clothing and Names"
## [50] "DKEFS Verbal Fluency Switching Fruits/Veget"
## [51] "Finger Tapper Dom Hand"
## [52] "Finger Tapper NonDom Hand"
## [53] "Grooved Pegboard Dom Hand"
## [54] "Grooved Pegboard Left Hand, Sec"
## [55] "Grooved Pegboard Nondom Hand"
## [56] "Grooved Pegboard Right Hand, Sec"
## [57] "Grooved Pegboard, Sec"
## [58] "Hopkins Verbal Learning Test Total"
## [59] "Letter-Number Sequencing WAIS-III"
## [60] "Letter Cancellation"
## [61] "MAE Controlled Oral Word Association"
## [62] "PASAT (Rao) 2 Sec Pacing"
## [63] "PASAT (Rao) 3 Sec Pacing"
## [64] "PASAT Number Correct"
## [65] "RAVL Delayed Recall"
## [66] "RAVL Total Score"
## [67] "RCF Delayed Recall"
## [68] "RCF Immediate Recall"
## [69] "Rey Copy"
## [70] "RVLT Delayed Recall"
## [71] "RVLT Delayed Recognition"
## [72] "Stroop"
## [73] "Symbol Search WAIS-III"
## [74] "TMT A"
## [75] "TMT B"
## [76] "Verbal Fluency FAS Number Correct"
## [77] "WAIS-III Arithmetic"
## [78] "WAIS-III Block Design"
## [79] "WAIS-III Digit Span"
## [80] "WAIS-III Digit Symbol"
## [81] "WAIS-III Digit Symbol Coding"
## [82] "WAIS-III Letter-Number"
## [83] "WAIS-III Symbol Search"
## [84] "WAIS-R Arithmetic"
## [85] "WAIS-R Block Design"
## [86] "WAIS-R Digit Span"
## [87] "WAIS-R Digit Symbol"
## [88] "WAIS-R Similarities"
## [89] "WASI Block Design"
## [90] "WASI Vocabulary"
## [91] "WCST Sorts Divided by Trials"
## [92] "WMS-III Digit Span Backwards"
## [93] "WMS-III Digit Span Forward"
## [94] "WMS-III Faces I"
## [95] "WMS-III Faces II"
## [96] "WMS-III Family Pictures II"
## [97] "WMS-III Letter Number Sequencing"
## [98] "WMS-III Logical Memory I"
## [99] "WMS-III Logical Memory II"
## [100] "WMS-III Spatial Span"
## [101] "WMS-III Spatial Span Backwards"
## [102] "WMS-III Spatial Span Forwards"
## [103] "WMS-III Story Delayed Recall"
## [104] "WMS-III Story Immediate Recall"
## [105] "WRAT-3 Reading Score"Create slab variable for study label. Add additional information for Bender.
D <- D[, slab := sprintf("%s: %s", author, test)]
D <- D[author == "Bender" & age == 40.11,
slab := sprintf("%s (%s)", slab, "CT alone")]
D <- D[author == "Bender" & age == 44.13,
slab := sprintf("%s (%s)", slab, "CT + tamoxifen")]
D <- D[, `:=` (treatmentGroup = NULL)]Split the data into two versions
DLongLongitudinal (including all time points)DPrepostPre-post (pre-treatment and 12+ month post-treatment)
D[, .N, .(author, monthsPostTx)][order(author, monthsPostTx)]## author monthsPostTx N
## 1: Ahles 0.0 35
## 2: Ahles 1.0 35
## 3: Ahles 6.0 35
## 4: Ahles 18.0 35
## 5: Bender 0.0 14
## 6: Bender 6.0 14
## 7: Bender 18.0 14
## 8: Collins 0.0 21
## 9: Collins 5.0 21
## 10: Collins 18.0 21
## 11: Dumas 0.0 8
## 12: Dumas 12.0 8
## 13: Fan 0.0 2
## 14: Fan 12.0 2
## 15: Fan 24.0 2
## 16: Jenkins 0.0 13
## 17: Jenkins 1.0 13
## 18: Jenkins 12.0 13
## 19: McDonald 0.0 4
## 20: McDonald 12.0 4
## 21: Moore 0.0 2
## 22: Moore 1.0 2
## 23: Moore 12.0 2
## 24: Tager 0.0 14
## 25: Tager 6.0 14
## 26: Tager 12.0 14
## 27: Wefel 2004 0.0 10
## 28: Wefel 2004 6.0 10
## 29: Wefel 2004 18.0 10
## 30: Wefel 2010 0.0 6
## 31: Wefel 2010 13.1 6
## author monthsPostTx NDLong <- D
DPre <- D[monthsPostTx == 0]
DPre [, .N, .(author, monthsPostTx)][order(author, monthsPostTx)]## author monthsPostTx N
## 1: Ahles 0 35
## 2: Bender 0 14
## 3: Collins 0 21
## 4: Dumas 0 8
## 5: Fan 0 2
## 6: Jenkins 0 13
## 7: McDonald 0 4
## 8: Moore 0 2
## 9: Tager 0 14
## 10: Wefel 2004 0 10
## 11: Wefel 2010 0 6DPost <- D[12 <= monthsPostTx]
DPost[, .N, .(author, monthsPostTx)][order(author, monthsPostTx)]## author monthsPostTx N
## 1: Ahles 18.0 35
## 2: Bender 18.0 14
## 3: Collins 18.0 21
## 4: Dumas 12.0 8
## 5: Fan 12.0 2
## 6: Fan 24.0 2
## 7: Jenkins 12.0 13
## 8: McDonald 12.0 4
## 9: Moore 12.0 2
## 10: Tager 12.0 14
## 11: Wefel 2004 18.0 10
## 12: Wefel 2010 13.1 6key <- c("author", "age", "ageCentered", "domain", "test", "isHigherWorse", "scoreType", "slab")
setkeyv(DPre , key)
setkeyv(DPost, key)
DPrepost <- merge(DPre, DPost, suffixes=c("Pre", "Post"))If the cognitive test where high values are worse, then flip the signs so the pre-post difference will have the same direction as tests where high values are better.
DPrepost <- DPrepost[isHigherWorse == TRUE,
`:=` (meanPre = -meanPre,
meanPost = -meanPost)]
message(sprintf("%d rows were flipped", nrow(DPrepost[isHigherWorse == TRUE])))## 26 rows were flippedCalculate effect sizes.
calcFixed <- function (D) {
escalc("SMD", data=D,
m1i=meanPost, sd1i=sdPost, n1i=nPost,
m2i=meanPre, sd2i=sdPre, n2i=nPre)
}
l <- list(calcFixed(DPrepost[domain == "Attn/Wkg Mem/Concen"]),
calcFixed(DPrepost[domain == "Verb Mem"]),
calcFixed(DPrepost[domain == "Vis Mem"]),
calcFixed(DPrepost[domain == "Verb Ability/Lang"]),
calcFixed(DPrepost[domain == "Motor Speed"]),
calcFixed(DPrepost[domain == "Info Proc Speed"]),
calcFixed(DPrepost[domain == "Exec Fxn"]),
calcFixed(DPrepost[domain == "Visuospatial"]))
DPrepost <- rbindlist(l)Order the data.
setorder(DPrepost, domain, author, test)Remove studies with missing data.
unique(DPrepost[is.na(yi), .(author, domain, test, yi)])## author domain test yi
## 1: Fan Attn/Wkg Mem/Concen TMT A NA
## 2: Fan Exec Fxn TMT B NADPrepost <- DPrepost[!is.na(yi)]Add id variable. Will need this for the random effect.
DPrepost <- DPrepost[, id := factor(1:nrow(DPrepost))]Save working data tables to file.
metadataPrepost <- makeMetadata(DPrepost)
metadataLong <- makeMetadata(DLong)
f <- sprintf("%s/%s", pathOut, "AllStudies.RData")
save(DPrepost, metadataPrepost, DLong, metadataLong, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Output/AllStudies.RData saved on: 2016-01-13 09:35:04
## File size: 56.354 KB5 Meta-analysis, pre-post
Standardized mean differences (SMD) between pre-treatment and 12+ month post-treatment cognitive impairment measures is modeled with a multilevel mixed effects model. Cognitive domain is modeled as a fixed effect, with one effect size for each of the 8 domains. In our meta-analysis, we have multiple SMDs from each study (one for each cognitive test reported). Instead of modeling the random effect as a single parameter (as we would if we only had one observed SMD per study), we partition the random effect into variance components for observed SMD i and for study. The two variance components allow for the computation of an intraclass correlation. In addition, study-level mean age is included as a covariate. Age is centered around a mean of 50.9.
Mathematically, the model is represented as
A second model to estimate a global SMD is
Models were estimated using the rma.mv() function from the metafor package for R.
citation("metafor")##
## To cite the metafor package in publications, please use:
##
## Viechtbauer, W. (2010). Conducting meta-analyses in R with the
## metafor package. Journal of Statistical Software, 36(3), 1-48.
## URL: http://www.jstatsoft.org/v36/i03/
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {Conducting meta-analyses in {R} with the {metafor} package},
## author = {Wolfgang Viechtbauer},
## journal = {Journal of Statistical Software},
## year = {2010},
## volume = {36},
## number = {3},
## pages = {1--48},
## url = {http://www.jstatsoft.org/v36/i03/},
## }An additional reference is
Konstantopoulos, S. (2011), Fixed effects and variance components estimation in three-level meta-analysis. Res. Synth. Method, 2: 61-76. doi: 10.1002/jrsm.35
Load tidy data.
f <- sprintf("%s/%s", pathOut, "AllStudies.RData")
load(f, verbose=TRUE)## Loading objects:
## DPrepost
## metadataPrepost
## DLong
## metadataLongmetadataPrepost$timeStamp## [1] "2016-01-13 09:35:04 PST"metadataPrepost$colNames## [1] "author" "age" "ageCentered"
## [4] "domain" "test" "isHigherWorse"
## [7] "scoreType" "slab" "monthsPostTxPre"
## [10] "nPre" "meanPre" "sdPre"
## [13] "monthsPostTxPost" "nPost" "meanPost"
## [16] "sdPost" "yi" "vi"
## [19] "id"D <- DPrepostM1models domain SMDsM2models the global SMD
randomEffect <- list(~ 1 | id, ~ 1 | author)
M1 <- rma.mv(yi ~ domain - 1 + ageCentered, vi, data=D, random=randomEffect, slab=slab)
M2 <- rma.mv(yi ~ ageCentered, vi, data=D, random=randomEffect)betasM1 <- data.frame(M1[c("b", "se", "zval", "pval", "ci.lb", "ci.ub")])
betasM2 <- data.frame(M2[c("b", "se", "zval", "pval", "ci.lb", "ci.ub")])
j <- grep("domain", rownames(M1$b))
summary <- rbind(data.frame(studies = D[, .(studies = uniqueN(author)), domain][, studies],
tests = D[, .N, domain][, N],
betasM1[j, ]),
data.frame(studies = D[, .(studies = uniqueN(author))][, studies],
tests = D[, .N],
betasM2[1, ]))
rownames(summary) <- gsub("domain", "", rownames(summary))
rownames(summary) <- gsub("intrcpt", "**GLOBAL**", rownames(summary))5.1 Pooled domain effects
| studies | tests | b | se | zval | pval | ci.lb | ci.ub | |
|---|---|---|---|---|---|---|---|---|
| Attn/Wkg Mem/Concen | 9 | 49 | 0.026 | 0.082 | 0.311 | 0.7561 | -0.136 | 0.187 |
| Exec Fxn | 6 | 14 | 0.114 | 0.140 | 0.812 | 0.4167 | -0.161 | 0.388 |
| Info Proc Speed | 7 | 12 | 0.155 | 0.158 | 0.982 | 0.3263 | -0.154 | 0.464 |
| Motor Speed | 4 | 10 | -0.021 | 0.173 | -0.121 | 0.9034 | -0.361 | 0.319 |
| Verb Ability/Lang | 5 | 10 | 0.316 | 0.168 | 1.879 | 0.0603 | -0.014 | 0.646 |
| Verb Mem | 6 | 17 | 0.652 | 0.133 | 4.915 | 0.0000 | 0.392 | 0.912 |
| Vis Mem | 4 | 11 | 0.642 | 0.167 | 3.842 | 0.0001 | 0.314 | 0.969 |
| Visuospatial | 4 | 4 | 0.343 | 0.270 | 1.274 | 0.2028 | -0.185 | 0.872 |
| GLOBAL | 10 | 127 | 0.228 | 0.055 | 4.124 | 0.0000 | 0.120 | 0.337 |
The intraclass correlation within study is 4.62e-09.
summary(M1)##
## Multivariate Meta-Analysis Model (k = 127; method: REML)
##
## logLik Deviance AIC BIC AICc
## -126.9694 253.9387 275.9387 306.4163 278.4293
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2207 0.4698 127 no id
## sigma^2.2 0.0000 0.0000 10 no author
##
## Test for Residual Heterogeneity:
## QE(df = 118) = 464.2018, p-val < .0001
##
## Test of Moderators (coefficient(s) 1,2,3,4,5,6,7,8,9):
## QM(df = 9) = 44.8211, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## domainAttn/Wkg Mem/Concen 0.0255 0.0822 0.3106 0.7561 -0.1356
## domainExec Fxn 0.1137 0.1400 0.8122 0.4167 -0.1607
## domainInfo Proc Speed 0.1548 0.1577 0.9815 0.3263 -0.1543
## domainMotor Speed -0.0211 0.1735 -0.1214 0.9034 -0.3610
## domainVerb Ability/Lang 0.3164 0.1684 1.8788 0.0603 -0.0137
## domainVerb Mem 0.6524 0.1327 4.9153 <.0001 0.3922
## domainVis Mem 0.6416 0.1670 3.8419 0.0001 0.3143
## domainVisuospatial 0.3434 0.2697 1.2736 0.2028 -0.1851
## ageCentered -0.0095 0.0098 -0.9703 0.3319 -0.0287
## ci.ub
## domainAttn/Wkg Mem/Concen 0.1867
## domainExec Fxn 0.3882
## domainInfo Proc Speed 0.4638
## domainMotor Speed 0.3189
## domainVerb Ability/Lang 0.6465 .
## domainVerb Mem 0.9125 ***
## domainVis Mem 0.9690 ***
## domainVisuospatial 0.8719
## ageCentered 0.0097
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(M2)##
## Multivariate Meta-Analysis Model (k = 127; method: REML)
##
## logLik Deviance AIC BIC AICc
## -141.8253 283.6506 291.6506 302.9638 291.9839
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2621 0.5120 127 no id
## sigma^2.2 0.0000 0.0000 10 no author
##
## Test for Residual Heterogeneity:
## QE(df = 125) = 533.7934, p-val < .0001
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 2.2391, p-val = 0.1346
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2282 0.0553 4.1241 <.0001 0.1198 0.3367 ***
## ageCentered -0.0153 0.0102 -1.4964 0.1346 -0.0354 0.0047
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save working data tables to file.
metadata <- makeMetadata(D)
f <- sprintf("%s/%s", pathOut, "metaAnalysisCognitiveImpairment.RData")
save(D, metadata, M1, M2, summary, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Output/metaAnalysisCognitiveImpairment.RData saved on: 2016-01-13 09:35:05
## File size: 63.552 KBf <- sprintf("%s/%s", pathOut, "metaAnalysisCognitiveImpairment-Data.csv")
write.csv(D, file=f, row.names=FALSE)
f <- sprintf("%s/%s", pathOut, "metaAnalysisCognitiveImpairment-summary.csv")
write.csv(summary, file=f, row.names=FALSE)
5.3 Diagnostic plots
Check the profile likelihoods of the variance and correlation components.
## Profiling sigma2 = 1
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Funnel plot to check for publication bias. See BMJ 2011;342:d4002 for a guide to interpret funnel plots.
## png
## 2
Publication bias does not appear to be a great concern.
6 Epilogue
## Sourcing https://gist.githubusercontent.com/benjamin-chan/80149dd4cdb16b2760ec/raw/a1fafde5c5086024dd01d410cc2f72fb82e93f26/sessionInfo.R
## SHA-1 hash of file is 41209357693515acefb05d4b209340e744a1cbe4## $timeStart
## [1] "2016-01-13 09:35:00"
##
## $timeEnd
## [1] "2016-01-13 09:35:19 PST"
##
## $timeElapsed
## [1] "18.34283 secs"
##
## $Sys.info
## sysname release
## "Windows" "7 x64"
## version nodename
## "build 7601, Service Pack 1" "GHBA299"
## machine login
## "x86-64" "chanb"
## user effective_user
## "chanb" "chanb"
##
## $sessionInfo
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] tools stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] extrafont_0.17 DiagrammeR_0.7 readstata13_0.7.1
## [4] metafor_1.9-8 Matrix_1.2-2 xtable_1.7-4
## [7] haven_0.2.0 googlesheets_0.1.0 openxlsx_3.0.0
## [10] data.table_1.9.6 devtools_1.8.0 RevoUtilsMath_3.2.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.1 cellranger_1.0.0 formatR_1.2 git2r_0.11.0
## [5] digest_0.6.8 evaluate_0.7.2 jsonlite_0.9.16 memoise_0.2.1
## [9] lattice_0.20-33 DBI_0.3.1 rstudioapi_0.3.1 curl_0.9.3
## [13] yaml_2.1.13 parallel_3.2.2 Rttf2pt1_1.3.3 dplyr_0.4.3
## [17] httr_1.0.0 stringr_1.0.0 xml2_0.1.2 knitr_1.11
## [21] htmlwidgets_0.5 rversions_1.0.2 grid_3.2.2 R6_2.1.1
## [25] rmarkdown_0.7 extrafontdb_1.0 magrittr_1.5 htmltools_0.2.6
## [29] assertthat_0.1 stringi_0.5-5 chron_2.3-47